Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations858
Missing cells3622
Missing cells (%)11.7%
Duplicate rows20
Duplicate rows (%)2.3%
Total size in memory241.4 KiB
Average record size in memory288.2 B

Variable types

Numeric10
Categorical26

Alerts

STDs:cervical condylomatosis has constant value "0.0"Constant
STDs:AIDS has constant value "0.0"Constant
Dataset has 20 (2.3%) duplicate rowsDuplicates
Age is highly overall correlated with Num of pregnancies and 1 other fieldsHigh correlation
Biopsy is highly overall correlated with Hinselmann and 1 other fieldsHigh correlation
Dx is highly overall correlated with Dx:CIN and 4 other fieldsHigh correlation
Dx:CIN is highly overall correlated with Dx and 2 other fieldsHigh correlation
Dx:Cancer is highly overall correlated with Dx and 1 other fieldsHigh correlation
Dx:HPV is highly overall correlated with Dx and 1 other fieldsHigh correlation
Hinselmann is highly overall correlated with Biopsy and 1 other fieldsHigh correlation
IUD is highly overall correlated with IUD (years)High correlation
IUD (years) is highly overall correlated with IUDHigh correlation
Num of pregnancies is highly overall correlated with AgeHigh correlation
STDs is highly overall correlated with STDs (number) and 5 other fieldsHigh correlation
STDs (number) is highly overall correlated with STDs and 6 other fieldsHigh correlation
STDs: Number of diagnosis is highly overall correlated with STDs and 4 other fieldsHigh correlation
STDs: Time since first diagnosis is highly overall correlated with Dx and 3 other fieldsHigh correlation
STDs: Time since last diagnosis is highly overall correlated with Age and 4 other fieldsHigh correlation
STDs:HIV is highly overall correlated with STDs (number) and 1 other fieldsHigh correlation
STDs:condylomatosis is highly overall correlated with STDs and 3 other fieldsHigh correlation
STDs:syphilis is highly overall correlated with STDs (number)High correlation
STDs:vaginal condylomatosis is highly overall correlated with STDs (number)High correlation
STDs:vulvo-perineal condylomatosis is highly overall correlated with STDs and 3 other fieldsHigh correlation
Schiller is highly overall correlated with Biopsy and 1 other fieldsHigh correlation
Smokes is highly overall correlated with Smokes (years)High correlation
Smokes (packs/year) is highly overall correlated with Smokes (years)High correlation
Smokes (years) is highly overall correlated with Smokes and 1 other fieldsHigh correlation
STDs is highly imbalanced (51.6%)Imbalance
STDs (number) is highly imbalanced (72.7%)Imbalance
STDs:condylomatosis is highly imbalanced (67.9%)Imbalance
STDs:vaginal condylomatosis is highly imbalanced (95.2%)Imbalance
STDs:vulvo-perineal condylomatosis is highly imbalanced (68.4%)Imbalance
STDs:syphilis is highly imbalanced (83.7%)Imbalance
STDs:pelvic inflammatory disease is highly imbalanced (98.5%)Imbalance
STDs:genital herpes is highly imbalanced (98.5%)Imbalance
STDs:molluscum contagiosum is highly imbalanced (98.5%)Imbalance
STDs:HIV is highly imbalanced (83.7%)Imbalance
STDs:Hepatitis B is highly imbalanced (98.5%)Imbalance
STDs:HPV is highly imbalanced (97.3%)Imbalance
STDs: Number of diagnosis is highly imbalanced (78.2%)Imbalance
Dx:Cancer is highly imbalanced (85.3%)Imbalance
Dx:CIN is highly imbalanced (91.6%)Imbalance
Dx:HPV is highly imbalanced (85.3%)Imbalance
Dx is highly imbalanced (81.6%)Imbalance
Hinselmann is highly imbalanced (75.4%)Imbalance
Schiller is highly imbalanced (57.6%)Imbalance
Citology is highly imbalanced (70.8%)Imbalance
Biopsy is highly imbalanced (65.6%)Imbalance
Number of sexual partners has 26 (3.0%) missing valuesMissing
Num of pregnancies has 56 (6.5%) missing valuesMissing
Smokes has 13 (1.5%) missing valuesMissing
Smokes (years) has 13 (1.5%) missing valuesMissing
Smokes (packs/year) has 13 (1.5%) missing valuesMissing
Hormonal Contraceptives has 108 (12.6%) missing valuesMissing
Hormonal Contraceptives (years) has 108 (12.6%) missing valuesMissing
IUD has 117 (13.6%) missing valuesMissing
IUD (years) has 117 (13.6%) missing valuesMissing
STDs has 105 (12.2%) missing valuesMissing
STDs (number) has 105 (12.2%) missing valuesMissing
STDs:condylomatosis has 105 (12.2%) missing valuesMissing
STDs:cervical condylomatosis has 105 (12.2%) missing valuesMissing
STDs:vaginal condylomatosis has 105 (12.2%) missing valuesMissing
STDs:vulvo-perineal condylomatosis has 105 (12.2%) missing valuesMissing
STDs:syphilis has 105 (12.2%) missing valuesMissing
STDs:pelvic inflammatory disease has 105 (12.2%) missing valuesMissing
STDs:genital herpes has 105 (12.2%) missing valuesMissing
STDs:molluscum contagiosum has 105 (12.2%) missing valuesMissing
STDs:AIDS has 105 (12.2%) missing valuesMissing
STDs:HIV has 105 (12.2%) missing valuesMissing
STDs:Hepatitis B has 105 (12.2%) missing valuesMissing
STDs:HPV has 105 (12.2%) missing valuesMissing
STDs: Time since first diagnosis has 787 (91.7%) missing valuesMissing
STDs: Time since last diagnosis has 787 (91.7%) missing valuesMissing
Num of pregnancies has 16 (1.9%) zerosZeros
Smokes (years) has 722 (84.1%) zerosZeros
Smokes (packs/year) has 722 (84.1%) zerosZeros
Hormonal Contraceptives (years) has 269 (31.4%) zerosZeros
IUD (years) has 658 (76.7%) zerosZeros

Reproduction

Analysis started2024-07-19 01:28:43.257102
Analysis finished2024-07-19 01:28:58.924085
Duration15.67 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.820513
Minimum13
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:28:59.028125image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q120
median25
Q332
95-th percentile41
Maximum84
Range71
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.4979481
Coefficient of variation (CV)0.3168451
Kurtosis4.7785751
Mean26.820513
Median Absolute Deviation (MAD)5.5
Skewness1.3942788
Sum23012
Variance72.215121
MonotonicityNot monotonic
2024-07-18T19:28:59.178119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
23 54
 
6.3%
18 50
 
5.8%
21 46
 
5.4%
20 45
 
5.2%
19 44
 
5.1%
24 39
 
4.5%
25 39
 
4.5%
26 38
 
4.4%
28 37
 
4.3%
17 35
 
4.1%
Other values (34) 431
50.2%
ValueCountFrequency (%)
13 1
 
0.1%
14 5
 
0.6%
15 21
2.4%
16 23
2.7%
17 35
4.1%
18 50
5.8%
19 44
5.1%
20 45
5.2%
21 46
5.4%
22 30
3.5%
ValueCountFrequency (%)
84 1
0.1%
79 1
0.1%
70 2
0.2%
59 1
0.1%
52 2
0.2%
51 1
0.1%
50 1
0.1%
49 2
0.2%
48 2
0.2%
47 1
0.1%

Number of sexual partners
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)1.4%
Missing26
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean2.5276442
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:28:59.403119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum28
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6677605
Coefficient of variation (CV)0.65980823
Kurtosis69.204754
Mean2.5276442
Median Absolute Deviation (MAD)1
Skewness5.4546486
Sum2103
Variance2.781425
MonotonicityNot monotonic
2024-07-18T19:28:59.521119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 272
31.7%
3 208
24.2%
1 206
24.0%
4 78
 
9.1%
5 44
 
5.1%
6 9
 
1.0%
7 7
 
0.8%
8 4
 
0.5%
15 1
 
0.1%
10 1
 
0.1%
Other values (2) 2
 
0.2%
(Missing) 26
 
3.0%
ValueCountFrequency (%)
1 206
24.0%
2 272
31.7%
3 208
24.2%
4 78
 
9.1%
5 44
 
5.1%
6 9
 
1.0%
7 7
 
0.8%
8 4
 
0.5%
9 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
28 1
 
0.1%
15 1
 
0.1%
10 1
 
0.1%
9 1
 
0.1%
8 4
 
0.5%
7 7
 
0.8%
6 9
 
1.0%
5 44
 
5.1%
4 78
 
9.1%
3 208
24.2%

First sexual intercourse
Real number (ℝ)

Distinct21
Distinct (%)2.5%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean16.9953
Minimum10
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:28:59.648119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q115
median17
Q318
95-th percentile22
Maximum32
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8033554
Coefficient of variation (CV)0.16494886
Kurtosis4.28836
Mean16.9953
Median Absolute Deviation (MAD)2
Skewness1.5643746
Sum14463
Variance7.8588014
MonotonicityNot monotonic
2024-07-18T19:28:59.771082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15 163
19.0%
17 151
17.6%
18 137
16.0%
16 121
14.1%
14 79
9.2%
19 60
 
7.0%
20 37
 
4.3%
13 25
 
2.9%
21 20
 
2.3%
23 9
 
1.0%
Other values (11) 49
 
5.7%
ValueCountFrequency (%)
10 2
 
0.2%
11 2
 
0.2%
12 6
 
0.7%
13 25
 
2.9%
14 79
9.2%
15 163
19.0%
16 121
14.1%
17 151
17.6%
18 137
16.0%
19 60
 
7.0%
ValueCountFrequency (%)
32 1
 
0.1%
29 5
 
0.6%
28 3
 
0.3%
27 6
 
0.7%
26 7
 
0.8%
25 2
 
0.2%
24 6
 
0.7%
23 9
1.0%
22 9
1.0%
21 20
2.3%

Num of pregnancies
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)1.4%
Missing56
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean2.2755611
Minimum0
Maximum11
Zeros16
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:28:59.898119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4474141
Coefficient of variation (CV)0.63606909
Kurtosis3.2133661
Mean2.2755611
Median Absolute Deviation (MAD)1
Skewness1.4235139
Sum1825
Variance2.0950075
MonotonicityNot monotonic
2024-07-18T19:29:00.014090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 270
31.5%
2 240
28.0%
3 139
16.2%
4 74
 
8.6%
5 35
 
4.1%
6 18
 
2.1%
0 16
 
1.9%
7 6
 
0.7%
8 2
 
0.2%
11 1
 
0.1%
(Missing) 56
 
6.5%
ValueCountFrequency (%)
0 16
 
1.9%
1 270
31.5%
2 240
28.0%
3 139
16.2%
4 74
 
8.6%
5 35
 
4.1%
6 18
 
2.1%
7 6
 
0.7%
8 2
 
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
11 1
 
0.1%
10 1
 
0.1%
8 2
 
0.2%
7 6
 
0.7%
6 18
 
2.1%
5 35
 
4.1%
4 74
 
8.6%
3 139
16.2%
2 240
28.0%
1 270
31.5%

Smokes
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing13
Missing (%)1.5%
Memory size6.8 KiB
0.0
722 
1.0
123 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2535
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 722
84.1%
1.0 123
 
14.3%
(Missing) 13
 
1.5%

Length

2024-07-18T19:29:00.150120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:00.270084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 722
85.4%
1.0 123
 
14.6%

Most occurring characters

ValueCountFrequency (%)
0 1567
61.8%
. 845
33.3%
1 123
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2535
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1567
61.8%
. 845
33.3%
1 123
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2535
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1567
61.8%
. 845
33.3%
1 123
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2535
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1567
61.8%
. 845
33.3%
1 123
 
4.9%

Smokes (years)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)3.6%
Missing13
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1.2197214
Minimum0
Maximum37
Zeros722
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:29:00.390119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.8
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.0890169
Coefficient of variation (CV)3.3524188
Kurtosis23.768418
Mean1.2197214
Median Absolute Deviation (MAD)0
Skewness4.4654839
Sum1030.6646
Variance16.72006
MonotonicityNot monotonic
2024-07-18T19:29:00.541118image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 722
84.1%
1.266972909 15
 
1.7%
5 9
 
1.0%
9 9
 
1.0%
1 8
 
0.9%
3 7
 
0.8%
2 7
 
0.8%
16 6
 
0.7%
7 6
 
0.7%
8 6
 
0.7%
Other values (20) 50
 
5.8%
(Missing) 13
 
1.5%
ValueCountFrequency (%)
0 722
84.1%
0.16 1
 
0.1%
0.5 3
 
0.3%
1 8
 
0.9%
1.266972909 15
 
1.7%
2 7
 
0.8%
3 7
 
0.8%
4 5
 
0.6%
5 9
 
1.0%
6 4
 
0.5%
ValueCountFrequency (%)
37 1
 
0.1%
34 1
 
0.1%
32 1
 
0.1%
28 1
 
0.1%
24 1
 
0.1%
22 2
0.2%
21 1
 
0.1%
20 1
 
0.1%
19 3
0.3%
18 1
 
0.1%

Smokes (packs/year)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct62
Distinct (%)7.3%
Missing13
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.45314395
Minimum0
Maximum37
Zeros722
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:29:00.688125image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.48
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2266098
Coefficient of variation (CV)4.913692
Kurtosis114.83971
Mean0.45314395
Median Absolute Deviation (MAD)0
Skewness9.3088062
Sum382.90664
Variance4.9577912
MonotonicityNot monotonic
2024-07-18T19:29:00.847084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 722
84.1%
0.5132021277 18
 
2.1%
1 6
 
0.7%
3 5
 
0.6%
2 4
 
0.5%
0.75 4
 
0.5%
1.2 4
 
0.5%
0.2 4
 
0.5%
0.05 4
 
0.5%
0.1 3
 
0.3%
Other values (52) 71
 
8.3%
(Missing) 13
 
1.5%
ValueCountFrequency (%)
0 722
84.1%
0.001 1
 
0.1%
0.003 1
 
0.1%
0.025 1
 
0.1%
0.04 2
 
0.2%
0.05 4
 
0.5%
0.1 3
 
0.3%
0.15 1
 
0.1%
0.16 2
 
0.2%
0.2 4
 
0.5%
ValueCountFrequency (%)
37 1
 
0.1%
22 1
 
0.1%
21 1
 
0.1%
19 1
 
0.1%
15 1
 
0.1%
12 3
0.3%
9 2
0.2%
8 2
0.2%
7.6 1
 
0.1%
7.5 1
 
0.1%

Hormonal Contraceptives
Categorical

MISSING 

Distinct2
Distinct (%)0.3%
Missing108
Missing (%)12.6%
Memory size6.8 KiB
1.0
481 
0.0
269 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2250
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 481
56.1%
0.0 269
31.4%
(Missing) 108
 
12.6%

Length

2024-07-18T19:29:00.992086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:01.101084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 481
64.1%
0.0 269
35.9%

Most occurring characters

ValueCountFrequency (%)
0 1019
45.3%
. 750
33.3%
1 481
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1019
45.3%
. 750
33.3%
1 481
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1019
45.3%
. 750
33.3%
1 481
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1019
45.3%
. 750
33.3%
1 481
21.4%

Hormonal Contraceptives (years)
Real number (ℝ)

MISSING  ZEROS 

Distinct40
Distinct (%)5.3%
Missing108
Missing (%)12.6%
Infinite0
Infinite (%)0.0%
Mean2.2564192
Minimum0
Maximum30
Zeros269
Zeros (%)31.4%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:29:01.226084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q33
95-th percentile9.55
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.7642535
Coefficient of variation (CV)1.6682421
Kurtosis9.0433797
Mean2.2564192
Median Absolute Deviation (MAD)0.5
Skewness2.6264377
Sum1692.3144
Variance14.169605
MonotonicityNot monotonic
2024-07-18T19:29:01.392084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 269
31.4%
1 77
 
9.0%
0.25 41
 
4.8%
2 40
 
4.7%
3 39
 
4.5%
5 34
 
4.0%
0.08 25
 
2.9%
0.5 25
 
2.9%
6 24
 
2.8%
4 22
 
2.6%
Other values (30) 154
17.9%
(Missing) 108
12.6%
ValueCountFrequency (%)
0 269
31.4%
0.08 25
 
2.9%
0.16 16
 
1.9%
0.17 1
 
0.1%
0.25 41
 
4.8%
0.33 9
 
1.0%
0.41 1
 
0.1%
0.42 8
 
0.9%
0.5 25
 
2.9%
0.58 6
 
0.7%
ValueCountFrequency (%)
30 1
 
0.1%
22 1
 
0.1%
20 4
0.5%
19 2
 
0.2%
17 1
 
0.1%
16 2
 
0.2%
15 6
0.7%
14 2
 
0.2%
13 2
 
0.2%
12 4
0.5%

IUD
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.3%
Missing117
Missing (%)13.6%
Memory size6.8 KiB
0.0
658 
1.0
83 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2223
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 658
76.7%
1.0 83
 
9.7%
(Missing) 117
 
13.6%

Length

2024-07-18T19:29:01.556086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:01.702084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 658
88.8%
1.0 83
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 1399
62.9%
. 741
33.3%
1 83
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1399
62.9%
. 741
33.3%
1 83
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1399
62.9%
. 741
33.3%
1 83
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1399
62.9%
. 741
33.3%
1 83
 
3.7%

IUD (years)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)3.5%
Missing117
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean0.51480432
Minimum0
Maximum19
Zeros658
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:29:01.838119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9430885
Coefficient of variation (CV)3.7744216
Kurtosis29.993328
Mean0.51480432
Median Absolute Deviation (MAD)0
Skewness5.0017585
Sum381.47
Variance3.7755931
MonotonicityNot monotonic
2024-07-18T19:29:01.998085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 658
76.7%
3 11
 
1.3%
2 10
 
1.2%
5 9
 
1.0%
1 8
 
0.9%
8 7
 
0.8%
7 7
 
0.8%
4 5
 
0.6%
6 5
 
0.6%
11 3
 
0.3%
Other values (16) 18
 
2.1%
(Missing) 117
 
13.6%
ValueCountFrequency (%)
0 658
76.7%
0.08 2
 
0.2%
0.16 1
 
0.1%
0.17 1
 
0.1%
0.25 1
 
0.1%
0.33 1
 
0.1%
0.41 1
 
0.1%
0.5 2
 
0.2%
0.58 1
 
0.1%
0.91 1
 
0.1%
ValueCountFrequency (%)
19 1
 
0.1%
17 1
 
0.1%
15 1
 
0.1%
12 1
 
0.1%
11 3
0.3%
10 1
 
0.1%
9 1
 
0.1%
8 7
0.8%
7 7
0.8%
6 5
0.6%

STDs
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
674 
1.0
79 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 674
78.6%
1.0 79
 
9.2%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:02.171110image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:02.286120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 674
89.5%
1.0 79
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 1427
63.2%
. 753
33.3%
1 79
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1427
63.2%
. 753
33.3%
1 79
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1427
63.2%
. 753
33.3%
1 79
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1427
63.2%
. 753
33.3%
1 79
 
3.5%

STDs (number)
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)0.7%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
674 
2.0
 
37
1.0
 
34
3.0
 
7
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 674
78.6%
2.0 37
 
4.3%
1.0 34
 
4.0%
3.0 7
 
0.8%
4.0 1
 
0.1%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:02.415120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:02.532127image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 674
89.5%
2.0 37
 
4.9%
1.0 34
 
4.5%
3.0 7
 
0.9%
4.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1427
63.2%
. 753
33.3%
2 37
 
1.6%
1 34
 
1.5%
3 7
 
0.3%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1427
63.2%
. 753
33.3%
2 37
 
1.6%
1 34
 
1.5%
3 7
 
0.3%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1427
63.2%
. 753
33.3%
2 37
 
1.6%
1 34
 
1.5%
3 7
 
0.3%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1427
63.2%
. 753
33.3%
2 37
 
1.6%
1 34
 
1.5%
3 7
 
0.3%
4 1
 
< 0.1%

STDs:condylomatosis
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
709 
1.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 709
82.6%
1.0 44
 
5.1%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:02.682086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:02.828083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 709
94.2%
1.0 44
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 1462
64.7%
. 753
33.3%
1 44
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1462
64.7%
. 753
33.3%
1 44
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1462
64.7%
. 753
33.3%
1 44
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1462
64.7%
. 753
33.3%
1 44
 
1.9%

STDs:cervical condylomatosis
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
753 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 753
87.8%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:02.993084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:03.133121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 753
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1506
66.7%
. 753
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1506
66.7%
. 753
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1506
66.7%
. 753
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1506
66.7%
. 753
33.3%

STDs:vaginal condylomatosis
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
749 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 749
87.3%
1.0 4
 
0.5%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:03.263119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:03.375119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 749
99.5%
1.0 4
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1502
66.5%
. 753
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1502
66.5%
. 753
33.3%
1 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1502
66.5%
. 753
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1502
66.5%
. 753
33.3%
1 4
 
0.2%

STDs:vulvo-perineal condylomatosis
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
710 
1.0
 
43

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 710
82.8%
1.0 43
 
5.0%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:03.489120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:03.597119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 710
94.3%
1.0 43
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 1463
64.8%
. 753
33.3%
1 43
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1463
64.8%
. 753
33.3%
1 43
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1463
64.8%
. 753
33.3%
1 43
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1463
64.8%
. 753
33.3%
1 43
 
1.9%

STDs:syphilis
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
735 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 735
85.7%
1.0 18
 
2.1%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:03.727087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:03.856113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 735
97.6%
1.0 18
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 1488
65.9%
. 753
33.3%
1 18
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1488
65.9%
. 753
33.3%
1 18
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1488
65.9%
. 753
33.3%
1 18
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1488
65.9%
. 753
33.3%
1 18
 
0.8%

STDs:pelvic inflammatory disease
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
752 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 752
87.6%
1.0 1
 
0.1%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:03.980121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:04.094088image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 752
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

STDs:genital herpes
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
752 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 752
87.6%
1.0 1
 
0.1%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:04.222121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:04.438119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 752
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

STDs:molluscum contagiosum
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
752 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 752
87.6%
1.0 1
 
0.1%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:04.556121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:04.670091image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 752
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

STDs:AIDS
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
753 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 753
87.8%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:04.799082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:04.909120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 753
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1506
66.7%
. 753
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1506
66.7%
. 753
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1506
66.7%
. 753
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1506
66.7%
. 753
33.3%

STDs:HIV
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
735 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 735
85.7%
1.0 18
 
2.1%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:05.028119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:05.147120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 735
97.6%
1.0 18
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 1488
65.9%
. 753
33.3%
1 18
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1488
65.9%
. 753
33.3%
1 18
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1488
65.9%
. 753
33.3%
1 18
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1488
65.9%
. 753
33.3%
1 18
 
0.8%

STDs:Hepatitis B
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
752 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 752
87.6%
1.0 1
 
0.1%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:05.266120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:05.380120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 752
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1505
66.6%
. 753
33.3%
1 1
 
< 0.1%

STDs:HPV
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing105
Missing (%)12.2%
Memory size6.8 KiB
0.0
751 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 751
87.5%
1.0 2
 
0.2%
(Missing) 105
 
12.2%

Length

2024-07-18T19:29:05.499120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:05.608121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 751
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1504
66.6%
. 753
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1504
66.6%
. 753
33.3%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1504
66.6%
. 753
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1504
66.6%
. 753
33.3%
1 2
 
0.1%

STDs: Number of diagnosis
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
787 
1
 
68
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Length

2024-07-18T19:29:05.718119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:05.832236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

STDs: Time since first diagnosis
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)25.4%
Missing787
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean6.1408451
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:29:05.951236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile19
Maximum22
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.895024
Coefficient of variation (CV)0.9599695
Kurtosis0.68227866
Mean6.1408451
Median Absolute Deviation (MAD)3
Skewness1.3261791
Sum436
Variance34.751308
MonotonicityNot monotonic
2024-07-18T19:29:06.068240image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 15
 
1.7%
3 10
 
1.2%
2 9
 
1.0%
4 6
 
0.7%
7 5
 
0.6%
16 4
 
0.5%
5 4
 
0.5%
8 3
 
0.3%
6 3
 
0.3%
19 2
 
0.2%
Other values (8) 10
 
1.2%
(Missing) 787
91.7%
ValueCountFrequency (%)
1 15
1.7%
2 9
1.0%
3 10
1.2%
4 6
 
0.7%
5 4
 
0.5%
6 3
 
0.3%
7 5
 
0.6%
8 3
 
0.3%
9 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
22 1
 
0.1%
21 2
0.2%
19 2
0.2%
18 1
 
0.1%
16 4
0.5%
15 1
 
0.1%
12 1
 
0.1%
11 2
0.2%
10 1
 
0.1%
9 1
 
0.1%

STDs: Time since last diagnosis
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)25.4%
Missing787
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean5.8169014
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-07-18T19:29:06.185236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37.5
95-th percentile18.5
Maximum22
Range21
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.7552705
Coefficient of variation (CV)0.98940486
Kurtosis1.0169533
Mean5.8169014
Median Absolute Deviation (MAD)2
Skewness1.4112042
Sum413
Variance33.123139
MonotonicityNot monotonic
2024-07-18T19:29:06.301238image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 17
 
2.0%
2 10
 
1.2%
3 9
 
1.0%
4 6
 
0.7%
7 5
 
0.6%
16 4
 
0.5%
5 3
 
0.3%
8 3
 
0.3%
6 3
 
0.3%
11 2
 
0.2%
Other values (8) 9
 
1.0%
(Missing) 787
91.7%
ValueCountFrequency (%)
1 17
2.0%
2 10
1.2%
3 9
1.0%
4 6
 
0.7%
5 3
 
0.3%
6 3
 
0.3%
7 5
 
0.6%
8 3
 
0.3%
9 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
22 1
 
0.1%
21 2
0.2%
19 1
 
0.1%
18 1
 
0.1%
16 4
0.5%
15 1
 
0.1%
12 1
 
0.1%
11 2
0.2%
10 1
 
0.1%
9 1
 
0.1%

Dx:Cancer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
840 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Length

2024-07-18T19:29:06.421237image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:06.529236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Dx:CIN
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
849 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Length

2024-07-18T19:29:06.640235image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:06.754193image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Dx:HPV
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
840 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Length

2024-07-18T19:29:06.876235image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:06.983204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Dx
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
834 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Length

2024-07-18T19:29:07.096240image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:07.200236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Hinselmann
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
823 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Length

2024-07-18T19:29:07.316236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:07.421243image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Schiller
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
784 
1
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Length

2024-07-18T19:29:07.532236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:07.641236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Citology
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
814 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Length

2024-07-18T19:29:07.754218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:07.867238image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Biopsy
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
803 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Length

2024-07-18T19:29:07.986236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T19:29:08.106206image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Interactions

2024-07-18T19:28:56.258087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:46.062118image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.275085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.415086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.521120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.620086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:51.823088image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.959121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:54.083086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.192119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:56.356085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:46.251120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.383103image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.527104image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.630120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.741086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:51.944088image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.075087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:54.208121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.287165image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:56.458123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:46.365084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.493145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.633085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.742085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.862085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.055086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.188146image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:54.318084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.376084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:56.567120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:46.472120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.596120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.742084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.870085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.970120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.167103image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.302110image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:54.427122image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.480084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:56.667119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:46.590085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.715085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.858111image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.979121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:51.080084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.283083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.411101image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:54.537087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.579122image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:56.754087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:46.714103image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.866083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.972122image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.096084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:51.200119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.403088image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.534084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:54.657132image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.667084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:56.895083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:46.847095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.981086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.084096image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.211086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:51.415123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.526085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.651090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:54.784083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.774086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:57.007083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:46.957121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.096087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.196089image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.318085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:51.526087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.642102image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.770089image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:54.895086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.880086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:57.102121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.079119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.216125image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.318119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.431110image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:51.646120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.768082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.889119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.014084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.975085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:57.197085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:47.179120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:48.319120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:49.422084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:50.527120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:51.734084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:52.865084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:53.991108image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:55.104086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-18T19:28:56.071083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-18T19:29:08.246235image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
AgeBiopsyCitologyDxDx:CINDx:CancerDx:HPVFirst sexual intercourseHinselmannHormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)Num of pregnanciesNumber of sexual partnersSTDsSTDs (number)STDs: Number of diagnosisSTDs: Time since first diagnosisSTDs: Time since last diagnosisSTDs:HIVSTDs:HPVSTDs:Hepatitis BSTDs:condylomatosisSTDs:genital herpesSTDs:molluscum contagiosumSTDs:pelvic inflammatory diseaseSTDs:syphilisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSchillerSmokesSmokes (packs/year)Smokes (years)
Age1.0000.0560.0000.2000.3340.0830.0750.4390.0000.1920.2630.2810.2890.5250.2140.0000.0000.0000.4290.5180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1070.0280.0570.064
Biopsy0.0561.0000.3150.1390.0830.1400.1400.0680.5350.0000.1910.0260.0900.0900.0000.0940.0850.1020.0000.0000.1040.0000.0000.0660.0490.0000.0000.0000.0000.0690.7240.0000.1060.049
Citology0.0000.3151.0000.0640.0000.0890.0890.0000.1760.0000.1740.0000.0000.0000.0000.0210.0000.0370.0000.0000.0450.0000.0000.0380.0000.0000.0000.0000.0000.0400.3510.0000.1290.000
Dx0.2000.1390.0641.0000.5720.6400.5910.0420.0420.0000.0000.1340.1450.0000.0000.0000.0000.0000.7020.7020.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0790.0480.0000.000
Dx:CIN0.3340.0830.0000.5721.0000.0000.0000.0000.0000.0000.0000.0150.0000.0230.0000.0000.0000.0000.9400.9400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Dx:Cancer0.0830.1400.0890.6400.0001.0000.8580.1020.1090.0000.1000.0900.1270.0000.0000.0000.0000.0000.3350.3350.0000.2430.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.2260.138
Dx:HPV0.0750.1400.0890.5910.0000.8581.0000.0330.1090.0000.1060.0270.0120.0000.0000.0000.0000.0000.3350.3350.0000.2430.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.2260.138
First sexual intercourse0.4390.0680.0000.0420.0000.1020.0331.0000.0310.0920.0800.000-0.018-0.020-0.1220.0000.0090.0000.0990.1350.0000.0000.0000.0350.0000.0000.0000.0600.1960.0370.0000.104-0.137-0.133
Hinselmann0.0000.5350.1760.0420.0000.1090.1090.0311.0000.0000.1370.0000.0640.0860.0000.0090.1550.1580.0000.0000.0580.0000.0000.0140.0000.0000.0000.0000.0000.0180.6390.0000.1500.079
Hormonal Contraceptives0.1920.0000.0000.0000.0000.0000.0000.0920.0001.0000.3950.0000.0840.2370.0410.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.019
Hormonal Contraceptives (years)0.2630.1910.1740.0000.0000.1000.1060.0800.1370.3951.0000.1550.0520.2800.0670.0000.0000.0000.1920.2460.0000.1120.0000.0000.0000.0000.0000.0600.0000.0000.1580.0640.0460.047
IUD0.2810.0260.0000.1340.0150.0900.0270.0000.0000.0000.1551.0000.8520.2450.0000.0320.0790.0000.2390.2710.0000.0000.0000.0590.0000.0000.0000.0000.0000.0390.0690.0340.0000.078
IUD (years)0.2890.0900.0000.1450.0000.1270.012-0.0180.0640.0840.0520.8521.0000.2440.0780.0000.0000.0000.2030.2370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1690.000-0.050-0.047
Num of pregnancies0.5250.0900.0000.0000.0230.0000.000-0.0200.0860.2370.2800.2450.2441.0000.1700.0660.0450.0000.3130.3720.0000.0000.0000.0750.0000.0340.0000.2110.0000.0650.1160.0980.0590.061
Number of sexual partners0.2140.0000.0000.0000.0000.0000.000-0.1220.0000.0410.0670.0000.0780.1701.0000.0870.0570.0000.2650.3040.0000.0000.0000.1470.0000.0420.0420.0000.0000.1490.0000.1850.2480.244
STDs0.0000.0940.0210.0000.0000.0000.0000.0000.0090.0000.0000.0320.0000.0660.0871.0000.9980.9411.0001.0000.4420.1020.0300.7180.0300.0300.0300.4420.1800.7090.0930.1150.1480.149
STDs (number)0.0000.0850.0000.0000.0000.0000.0000.0090.1550.0000.0000.0790.0000.0450.0570.9981.0000.8450.0000.0000.6210.2260.1430.9860.1510.1510.1510.6810.5650.9740.1330.1280.0280.131
STDs: Number of diagnosis0.0000.1020.0370.0000.0000.0000.0000.0000.1580.0000.0000.0000.0000.0000.0000.9410.8451.0000.0000.0000.5750.0380.0970.7030.0970.0970.0970.4400.2230.6930.1460.1030.0540.054
STDs: Time since first diagnosis0.4290.0000.0000.7020.9400.3350.3350.0990.0000.0000.1920.2390.2030.3130.2651.0000.0000.0001.0000.9250.1550.3350.4450.1630.0000.0000.4450.0660.0000.1420.2130.0000.1510.144
STDs: Time since last diagnosis0.5180.0000.0000.7020.9400.3350.3350.1350.0000.0000.2460.2710.2370.3720.3041.0000.0000.0000.9251.0000.0000.3350.4450.1440.0000.0000.4450.0000.0000.1090.2220.0000.1680.161
STDs:HIV0.0000.1040.0450.0000.0000.0000.0000.0000.0580.0620.0000.0000.0000.0000.0000.4420.6210.5750.1550.0001.0000.0000.1080.0830.0000.0000.0000.0000.0000.0850.1040.0380.1730.216
STDs:HPV0.0000.0000.0000.0550.0000.2430.2430.0000.0000.0000.1120.0000.0000.0000.0000.1020.2260.0380.3350.3350.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.093
STDs:Hepatitis B0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.1430.0970.4450.4450.1080.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.2720.295
STDs:condylomatosis0.0000.0660.0380.0000.0000.0000.0000.0350.0140.0000.0000.0590.0000.0750.1470.7180.9860.7030.1630.1440.0830.0000.0001.0000.0000.0000.0000.0000.2520.9760.0930.0440.0660.024
STDs:genital herpes0.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.1510.0970.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
STDs:molluscum contagiosum0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0420.0300.1510.0970.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
STDs:pelvic inflammatory disease0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0300.1510.0970.4450.4450.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
STDs:syphilis0.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0600.0000.0000.2110.0000.4420.6810.4400.0660.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0690.0000.000
STDs:vaginal condylomatosis0.0000.0000.0000.0000.0000.0000.0000.1960.0000.0170.0000.0000.0000.0000.0000.1800.5650.2230.0000.0000.0000.0000.0000.2520.0000.0000.0000.0001.0000.1750.0000.0320.1030.310
STDs:vulvo-perineal condylomatosis0.0000.0690.0400.0000.0000.0000.0000.0370.0180.0000.0000.0390.0000.0650.1490.7090.9740.6930.1420.1090.0850.0000.0000.9760.0000.0000.0000.0000.1751.0000.0970.0480.0690.034
Schiller0.1070.7240.3510.0790.0000.1390.1390.0000.6390.0000.1580.0690.1690.1160.0000.0930.1330.1460.2130.2220.1040.0000.0000.0930.0000.0000.0000.0000.0000.0971.0000.0340.0840.134
Smokes0.0280.0000.0000.0480.0000.0000.0000.1040.0000.0000.0640.0340.0000.0980.1850.1150.1280.1030.0000.0000.0380.0000.0060.0440.0000.0000.0000.0690.0320.0480.0341.0000.4410.788
Smokes (packs/year)0.0570.1060.1290.0000.0000.2260.226-0.1370.1500.0000.0460.000-0.0500.0590.2480.1480.0280.0540.1510.1680.1730.0000.2720.0660.0000.0000.0000.0000.1030.0690.0840.4411.0000.997
Smokes (years)0.0640.0490.0000.0000.0000.1380.138-0.1330.0790.0190.0470.078-0.0470.0610.2440.1490.1310.0540.1440.1610.2160.0930.2950.0240.0000.0000.0000.0000.3100.0340.1340.7880.9971.000

Missing values

2024-07-18T19:28:57.393119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-18T19:28:57.935120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-18T19:28:58.435086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:cervical condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:AIDSSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisSTDs: Time since first diagnosisSTDs: Time since last diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitologyBiopsy
0184.015.01.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
1151.014.01.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
2341.0NaN1.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
3525.016.04.01.037.00000037.01.03.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN10100000
4463.021.04.00.00.0000000.01.015.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
5423.023.02.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
6513.017.06.01.034.0000003.40.00.01.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00001101
7261.026.03.00.00.0000000.01.02.01.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
8451.020.05.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN10110000
9443.015.0NaN1.01.2669732.80.00.0NaNNaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:cervical condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:AIDSSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisSTDs: Time since first diagnosisSTDs: Time since last diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitologyBiopsy
848313.018.01.00.00.00.001.00.500.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
849323.018.01.01.011.00.161.06.000.00.01.01.00.00.00.00.00.00.00.00.00.00.00.01.00NaNNaN10100000
850191.014.00.00.00.00.000.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
851232.015.02.00.00.00.000.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
852433.017.03.00.00.00.001.05.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
853343.018.00.00.00.00.000.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
854322.019.01.00.00.00.001.08.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
855252.017.00.00.00.00.001.00.080.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000010
856332.024.02.00.00.00.001.00.080.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000
857292.020.01.00.00.00.001.00.500.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN00000000

Duplicate rows

Most frequently occurring

AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:cervical condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:AIDSSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisSTDs: Time since first diagnosisSTDs: Time since last diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitologyBiopsy# duplicates
0151.014.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN000000004
7172.015.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN000000003
1151.015.01.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaN000000002
2152.014.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN000000002
3161.014.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN000000002
4161.015.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN000000002
5171.016.01.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaN000000002
6171.017.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN000000002
8172.015.01.00.00.00.01.00.330.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN000000002
9181.014.02.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00NaNNaN000000002